Assessing communities of practice in health policy: a conceptual framework as a first step towards empirical research
Bibliographic record
Abstract
Communities of Practice (CoPs) are groups of people that interact regularly to deepen their knowledge on a specific topic. Thanks to information and communication technologies, CoPs can involve experts distributed across countries and adopt a 'transnational' membership. This has allowed the strategy to be applied to domains of knowledge such as health policy with a global perspective. CoPs represent a potentially valuable tool for producing and sharing explicit knowledge, as well as tacit knowledge and implementation practices. They may also be effective in creating links among the different 'knowledge holders' contributing to health policy (e.g., researchers, policymakers, technical assistants, practitioners, etc.). CoPs in global health are growing in number and activities. As a result, there is an increasing need to document their progress and evaluate their effectiveness. This paper represents a first step towards such empirical research as it aims to provide a conceptual framework for the analysis and assessment of transnational CoPs in health policy.The framework is developed based on the findings of a literature review as well as on our experience, and reflects the specific features and challenges of transnational CoPs in health policy. It organizes the key elements of CoPs into a logical flow that links available resources and the capacity to mobilize them, with knowledge management activities and the expansion of knowledge, with changes in policy and practice and, ultimately, with an improvement in health outcomes. Additionally, the paper addresses the challenges in the operationalization and empirical application of the framework.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.079 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.011 | 0.061 |
| Scholarly communication | 0.030 | 0.049 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".